group leader: Dr. Hernandez-Vargas

Recent Submissions

  • An Egyptian HPAI H5N1 isolate from clade 2.2.1.2 is highly pathogenic in an experimentally infected domestic duck breed (Sudani duck).

    Samir, M; Hamed, M; Abdallah, F; Kinh Nguyen, V; Hernandez-Vargas, E A; Seehusen, F; Baumgärtner, W; Hussein, A; Ali, A A H; Pessler,, F; TWINCORE, Zentrum für experimentelle uns klinische Ifektionsforschung GmbH, Feodor-Lynen-Str. 7, 30625 Hannover, Germany. (2018-01-24)
    The highly pathogenic avian influenza (HPAI) H5N1 viruses continue to cause major problems in poultry and can, although rarely, cause human infection. Being enzootic in domestic poultry, Egyptian isolates are continuously evolving, and novel clades vary in their pathogenicity in avian hosts. Considering the importance of domestic ducks as natural hosts of HPAI H5N1 viruses and their likelihood of physical contact with other avian hosts and humans, it is of utmost importance to characterize the pathogenicity of newly emerged HPAI strains in the domestic duck. The most recently identified Egyptian clade 2.2.1.2 HPAI H5N1 viruses have been isolated from naturally infected pigeons, turkeys and humans. However, essentially nothing is known about their pathogenicity in domestic ducks. We therefore characterized the pathogenicity of an Egyptian HPAI H5N1 isolate A/chicken/Faquos/amn12/2011 (clade 2.2.1.2) in Sudani duck, a domestic duck breed commonly reared in Egypt. While viral transcription (HA mRNA) was highest in lung, heart and kidney peaking between 40 and 48 hpi, lower levels were detected in brain. Weight loss of infected ducks started at 16 hpi and persisted until 120 hpi. The first severe clinical signs were noted by 32 hpi and peaked in severity at 72 and 96 hpi. Haematological analyses showed a decline in total leucocytes, granulocytes, platelets and granulocyte/lymphocyte ratio, but lymphocytosis. Upon necropsy, lesions were obvious in heart, liver, spleen and pancreas and consisted mainly of necrosis and petechial haemorrhage. Histologically, lungs were the most severely affected organs, whereas brain only showed mild neuronal degeneration and gliosis at 48 hpi despite obvious neurological clinical signs. Taken together, our results provide first evidence that this HPAI H5N1 isolate (clade 2.2.1.2) is highly pathogenic to Sudani ducks and highlight the importance of this breed as potential reservoir and disseminator of HPAI strains from this clade.
  • Windows of opportunity for Ebola virus infection treatment and vaccination.

    Nguyen, Van Kinh; Hernandez-Vargas, Esteban A; BRICS, Braunschweiger Zentrum für Systembiologie, Rebenring 56, 38106 Braunschweig, Germany. (2017-08-21)
    Ebola virus (EBOV) infection causes a high death toll, killing a high proportion of EBOV-infected patients within 7 days. Comprehensive data on EBOV infection are fragmented, hampering efforts in developing therapeutics and vaccines against EBOV. Under this circumstance, mathematical models become valuable resources to explore potential controlling strategies. In this paper, we employed experimental data of EBOV-infected nonhuman primates (NHPs) to construct a mathematical framework for determining windows of opportunity for treatment and vaccination. Considering a prophylactic vaccine based on recombinant vesicular stomatitis virus expressing the EBOV glycoprotein (rVSV-EBOV), vaccination could be protective if a subject is vaccinated during a period from one week to four months before infection. For the case of a therapeutic vaccine based on monoclonal antibodies (mAbs), a single dose might resolve the invasive EBOV replication even if it was administrated as late as four days after infection. Our mathematical models can be used as building blocks for evaluating therapeutic and vaccine modalities as well as for evaluating public health intervention strategies in outbreaks. Future laboratory experiments will help to validate and refine the estimates of the windows of opportunity proposed here.
  • Batch Cultivation Model for Biopolymer Production

    Torres-Cerna, C. E.; Alanis, A. Y.; Poblete-Castro, I.; Hernandez-Vargas, E. A.; BRICS, Braunschweiger Zentrum für Systembiology, Rbenring 56, 38106 Braunschweig, Germany. (2017-04-15)
  • Influenza epidemic surveillance and prediction based on electronic health record data from an out-of-hours general practitioner cooperative: model development and validation on 2003-2015 data.

    Michiels, Barbara; Nguyen, Van Kinh; Coenen, Samuel; Ryckebosch, Philippe; Bossuyt, Nathalie; Hens, Niel; BRICS - Braunschweig Integrated Centre of Systems Biology, Rebenring 56. 38106 Braunschweig, Germany. (2017-01-18)
    Annual influenza epidemics significantly burden health care. Anticipating them allows for timely preparation. The Scientific Institute of Public Health in Belgium (WIV-ISP) monitors the incidence of influenza and influenza-like illnesses (ILIs) and reports on a weekly basis. General practitioners working in out-of-hour cooperatives (OOH GPCs) register diagnoses of ILIs in an instantly accessible electronic health record (EHR) system. This article has two objectives: to explore the possibility of modelling seasonal influenza epidemics using EHR ILI data from the OOH GPC Deurne-Borgerhout, Belgium, and to attempt to develop a model accurately predicting new epidemics to complement the national influenza surveillance by WIV-ISP.
  • Analysis of Practical Identifiability of a Viral Infection Model.

    Nguyen, Van Kinh; Klawonn, Frank; Mikolajczyk, Rafael; Hernandez-Vargas, Esteban A.; Helmholtz Centre for infection research, Inhoffenstr. 7, 38124 Braunschweig, Germany. (2016)
    Mathematical modelling approaches have granted a significant contribution to life sciences and beyond to understand experimental results. However, incomplete and inadequate assessments in parameter estimation practices hamper the parameter reliability, and consequently the insights that ultimately could arise from a mathematical model. To keep the diligent works in modelling biological systems from being mistrusted, potential sources of error must be acknowledged. Employing a popular mathematical model in viral infection research, existing means and practices in parameter estimation are exemplified. Numerical results show that poor experimental data is a main source that can lead to erroneous parameter estimates despite the use of innovative parameter estimation algorithms. Arbitrary choices of initial conditions as well as data asynchrony distort the parameter estimates but are often overlooked in modelling studies. This work stresses the existence of several sources of error buried in reports of modelling biological systems, voicing the need for assessing the sources of error, consolidating efforts in solving the immediate difficulties, and possibly reconsidering the use of mathematical modelling to quantify experimental data.
  • Hierarchical effects of pro-inflammatory cytokines on the post-influenza susceptibility to pneumococcal coinfection.

    Duvigneau, Stefanie; Sharma-Chawla, Niharika; Boianelli, Alessandro; Stegemann-Koniszewski, Sabine; Nguyen, Van Kinh; Bruder, Dunja; Hernandez-Vargas, Esteban A.; BRICS, Braunschweiger Zentrum für Systembiologie, Rebenring 56, 38106 Braunschweig. Germany. (2016-11-22)
    In the course of influenza A virus (IAV) infections, a secondary bacterial infection frequently leads to serious respiratory conditions provoking high hospitalization and death tolls. Although abundant pro-inflammatory responses have been reported as key contributing factors for these severe dual infections, the relative contributions of cytokines remain largely unclear. In the current study, mathematical modelling based on murine experimental data dissects IFN-γ as a cytokine candidate responsible for impaired bacterial clearance, thereby promoting bacterial growth and systemic dissemination during acute IAV infection. We also found a time-dependent detrimental role of IL-6 in curtailing bacterial outgrowth which was not as distinct as for IFN-γ. Our numerical simulations suggested a detrimental effect of IFN-γ alone and in synergism with IL-6 but no conclusive pathogenic effect of IL-6 and TNF-α alone. This work provides a rationale to understand the potential impact of how to manipulate temporal immune components, facilitating the formulation of hypotheses about potential therapeutic strategies to treat coinfections.
  • Modeling Influenza Virus Infection: A Roadmap for Influenza Research.

    Boianelli, Alessandro; Nguyen, Van Kinh; Ebensen, Thomas; Schulze, Kai; Wilk, Esther; Sharma, Niharika; Stegemann-Koniszewski, Sabine; Bruder, Dunja; Toapanta, Franklin R; Guzmán, Carlos A; Meyer-Hermann, Michael; Hernandez-Vargas, Esteban A; BRICS, Braunschweiger Zentrum für Systembiologie, Rebenring 56, 38106 Braunschweig, Germany. (2015-10)
    Influenza A virus (IAV) infection represents a global threat causing seasonal outbreaks and pandemics. Additionally, secondary bacterial infections, caused mainly by Streptococcus pneumoniae, are one of the main complications and responsible for the enhanced morbidity and mortality associated with IAV infections. In spite of the significant advances in our knowledge of IAV infections, holistic comprehension of the interplay between IAV and the host immune response (IR) remains largely fragmented. During the last decade, mathematical modeling has been instrumental to explain and quantify IAV dynamics. In this paper, we review not only the state of the art of mathematical models of IAV infection but also the methodologies exploited for parameter estimation. We focus on the adaptive IR control of IAV infection and the possible mechanisms that could promote a secondary bacterial coinfection. To exemplify IAV dynamics and identifiability issues, a mathematical model to explain the interactions between adaptive IR and IAV infection is considered. Furthermore, in this paper we propose a roadmap for future influenza research. The development of a mathematical modeling framework with a secondary bacterial coinfection, immunosenescence, host genetic factors and responsiveness to vaccination will be pivotal to advance IAV infection understanding and treatment optimization.
  • Oseltamivir PK/PD Modeling and Simulation to Evaluate Treatment Strategies against Influenza-Pneumococcus Coinfection.

    Boianelli, Alessandro; Sharma-Chawla, Niharika; Bruder, Dunja; Hernandez-Vargas, Esteban A; Helmholtz Centre for infection research, Inhoffenstr. 7, 38124 Braunschweig, Germany. (2016)
    Influenza pandemics and seasonal outbreaks have shown the potential of Influenza A virus (IAV) to enhance susceptibility to a secondary infection with the bacterial pathogen Streptococcus pneumoniae (Sp). The high morbidity and mortality rate revealed the poor efficacy of antiviral drugs and vaccines to fight IAV infections. Currently, the most effective treatment for IAV is by antiviral neuraminidase inhibitors. Among them, the most frequently stockpiled is Oseltamivir which reduces viral release and transmission. However, effectiveness of Oseltamivir is compromised by the emergence of resistant IAV strains and secondary bacterial infections. To date, little attention has been given to evaluate how Oseltamivir treatment strategies alter Influenza viral infection in presence of Sp coinfection and a resistant IAV strain emergence. In this paper we investigate the efficacy of current approved Oseltamivir treatment regimens using a computational approach. Our numerical results suggest that the curative regimen (75 mg) may yield 47% of antiviral efficacy and 9% of antibacterial efficacy. An increment in dose to 150 mg (pandemic regimen) may increase the antiviral efficacy to 49% and the antibacterial efficacy to 16%. The choice to decrease the intake frequency to once per day is not recommended due to a significant reduction in both antiviral and antibacterial efficacy. We also observe that the treatment duration of 10 days may not provide a clear improvement on the antiviral and antibacterial efficacy compared to 5 days. All together, our in silico study reveals the success and pitfalls of Oseltamivir treatment strategies within IAV-Sp coinfection and calls for testing the validity in clinical trials.